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*The author of this computation has been verified*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Thu, 22 Dec 2011 12:51:13 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/22/t13245763281v1r22y9w0bz6y3.htm/, Retrieved Fri, 03 May 2024 14:33:55 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159782, Retrieved Fri, 03 May 2024 14:33:55 +0000

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Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Recursive Partitioning (Regression Trees)] [] [2011-12-22 17:51:13] [694c30abd2a3b2ee5cb46fc74cb5bfb9] [Current]

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Dataseries X:

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1845	162687	95	595	115	0	48	21	82	73	20465	6200	23975	39	37
1797	201906	63	545	76	1	58	20	80	56	33629	10265	85634	46	43
192	7215	18	72	1	0	0	0	0	0	1423	603	1929	0	0
2444	146367	97	679	155	0	67	27	84	63	25629	8874	36294	54	54
3567	257045	139	1201	125	0	83	31	124	116	54002	20323	72255	93	86
6953	528532	268	1975	278	1	137	36	140	138	151036	26258	189748	198	181
1873	191582	60	602	93	1	65	26	100	76	33287	10165	61834	42	42
1740	195674	60	496	59	0	86	30	115	107	31172	8247	68167	59	59
2079	177020	45	670	87	0	62	30	109	50	28113	8683	38462	49	46
3120	330255	100	1047	130	1	72	27	108	81	57803	16957	101219	83	77
1946	121844	75	634	158	2	50	24	63	58	49830	8058	43270	49	49
2370	203938	72	743	120	0	88	30	118	91	52143	20488	76183	83	79
1962	116737	108	692	87	0	62	22	71	41	21055	7945	31476	39	37
3198	220751	120	1086	264	4	79	28	112	100	47007	13448	62157	93	92
1496	173259	65	420	51	4	56	18	63	61	28735	5389	46261	31	31
1574	156326	89	474	85	3	54	22	86	74	59147	6185	50063	29	28
1808	145178	59	442	100	0	81	37	148	147	78950	24369	64483	104	103
1309	89171	61	373	72	5	13	15	54	45	13497	70	2341	2	2
2820	172624	88	899	147	0	74	34	134	110	46154	17327	48149	46	48
799	43391	28	253	49	0	18	18	57	41	53249	3878	12743	27	25
1162	87927	62	399	40	0	31	15	59	37	10726	3149	18743	16	16
2818	241285	103	850	99	0	99	30	113	84	83700	20517	97057	108	106
1780	200429	75	648	127	1	38	25	96	67	40400	2570	17675	36	35
2316	146946	57	717	164	1	59	34	96	69	33797	5162	33106	33	33
1994	159763	89	619	41	1	54	21	78	58	36205	5299	53311	46	45
1806	207078	34	657	160	0	63	21	80	60	30165	7233	42754	65	64
2153	212394	167	691	92	0	66	25	93	88	58534	15657	59056	80	73
1458	201536	96	366	59	0	90	31	109	75	44663	15329	101621	81	78
3000	394662	121	994	89	0	72	31	115	98	92556	14881	118120	69	63
2236	217892	46	929	90	0	61	20	79	67	40078	16318	79572	69	69
1685	182286	45	490	76	0	61	28	103	84	34711	9556	42744	37	36
1667	188748	48	574	116	2	63	22	71	62	31076	10462	65931	45	41
2257	137978	107	738	92	4	53	17	66	35	74608	7192	38575	62	59
3402	259627	132	1039	361	0	120	25	100	74	58092	4362	28795	33	33
2571	236489	55	844	85	1	73	25	100	93	42009	14349	94440	77	76
1	0	1	0	0	0	0	0	0	0	0	0	0	0	0
2143	230761	65	1000	63	0	54	31	121	87	36022	10881	38229	34	27
1878	132807	54	629	138	3	54	14	51	39	23333	8022	31972	44	44
2224	161703	52	547	270	9	46	35	119	101	53349	13073	40071	43	43
2186	253254	68	811	64	0	83	34	136	135	92596	26641	132480	117	104
2533	269329	72	837	96	2	106	22	84	76	49598	14426	62797	125	120
1823	161273	61	682	62	0	44	34	136	118	44093	15604	40429	49	44
1095	107181	33	400	35	2	27	23	84	76	84205	9184	45545	76	71
2303	213097	81	831	66	1	73	24	92	65	63369	5989	57568	81	78
1365	139667	51	419	56	2	71	26	103	97	60132	11270	39019	111	106
1245	171101	99	334	41	2	44	23	85	70	37403	13958	53866	61	61
756	81407	33	216	49	1	23	35	106	63	24460	7162	38345	56	53
2419	247596	106	786	121	0	78	24	96	96	46456	13275	50210	54	51
2327	239807	90	752	113	1	60	31	124	112	66616	21224	80947	47	46
2787	172743	60	964	190	8	73	30	106	82	41554	10615	43461	55	55
658	48188	28	205	37	0	12	22	82	39	22346	2102	14812	14	14
2013	169355	71	506	52	0	104	23	87	69	30874	12396	37819	44	44
2616	325322	77	830	89	0	86	27	97	93	68701	18717	102738	115	113
2072	241518	80	694	73	0	57	30	107	76	35728	9724	54509	57	55
1912	195583	60	691	49	1	67	33	126	117	29010	9863	62956	48	46
1775	159913	57	547	77	8	44	12	43	31	23110	8374	55411	40	39
1943	223936	71	547	58	0	53	26	96	65	38844	8030	50611	51	51
1047	101694	26	329	75	1	26	26	100	78	27084	7509	26692	32	31
1190	157258	68	427	32	0	67	23	91	87	35139	14146	60056	36	36
2932	211586	101	993	59	10	36	38	136	85	57476	7768	25155	47	47
1868	181076	66	564	71	6	56	32	128	119	33277	13823	42840	51	53
2255	150518	84	836	91	0	52	21	83	65	31141	7230	39358	37	38
1392	141491	64	376	87	11	54	22	74	60	61281	10170	47241	52	52
1355	130108	40	471	48	3	61	26	96	67	25820	7573	49611	42	37
1321	166351	38	431	63	0	27	28	102	94	23284	5753	41833	11	11
1526	124197	43	483	41	0	58	33	122	100	35378	9791	48930	47	45
2335	195043	71	504	86	8	76	36	144	135	74990	19365	110600	59	59
2898	138708	66	887	152	2	93	25	90	71	29653	9422	52235	82	82
1118	116552	40	271	49	0	59	25	97	78	64622	12310	53986	49	49
340	31970	15	101	40	0	5	21	78	42	4157	1283	4105	6	6
2977	258158	115	1097	135	3	57	19	72	42	29245	6372	59331	83	81
1452	151194	79	470	83	1	42	12	45	8	50008	5413	47796	56	56
1550	135926	68	528	62	2	88	30	120	86	52338	10837	38302	114	105
1685	119629	73	475	91	1	53	21	59	41	13310	3394	14063	46	46
2728	171518	71	698	95	0	81	39	150	131	92901	12964	54414	46	46
1574	108949	45	425	82	2	35	32	117	91	10956	3495	9903	2	2
2413	183471	60	709	112	1	102	28	123	102	34241	11580	53987	51	51
2563	159966	98	824	70	0	71	29	114	91	75043	9970	88937	96	95
1080	93786	35	336	78	0	28	21	75	46	21152	4911	21928	20	18
1235	84971	72	395	105	0	34	31	114	60	42249	10138	29487	57	55
980	88882	76	234	49	0	54	26	94	69	42005	14697	35334	49	48
2246	304603	65	830	60	0	49	29	116	95	41152	8464	57596	51	48
1076	75101	30	334	49	1	30	23	86	17	14399	4204	29750	40	39
1638	145043	41	524	132	0	57	25	90	61	28263	10226	41029	40	40
1208	95827	48	393	49	0	54	22	87	55	17215	3456	12416	36	36
1866	173924	59	574	71	0	38	26	99	55	48140	8895	51158	64	60
2727	241957	238	672	102	0	63	33	132	124	62897	22557	79935	117	114
1208	115367	115	284	74	0	58	24	96	73	22883	6900	26552	40	39
1427	118689	65	452	49	7	46	24	91	73	41622	8620	25807	46	45
1610	164078	54	653	74	0	46	21	77	67	40715	7820	50620	61	59
1865	158931	42	684	59	5	51	28	104	66	65897	12112	61467	59	59
2413	184139	83	706	91	1	87	28	100	77	76542	13178	65292	94	93
1238	152856	58	417	68	0	39	25	94	83	37477	7028	55516	36	35
1468	146159	61	551	81	0	28	15	60	55	53216	6616	42006	51	47
974	62535	43	394	33	0	26	13	46	27	40911	9570	26273	39	36
2319	245196	117	730	166	0	52	36	135	115	57021	14612	90248	62	59
1890	199841	71	571	97	0	96	27	99	85	73116	11219	61476	79	79
223	19349	12	67	15	0	13	1	2	0	3895	786	9604	14	14
2527	247280	109	877	105	3	43	24	96	83	46609	11252	45108	45	42
2105	164457	86	865	61	0	42	31	109	90	29351	9289	47232	43	41
778	72128	30	306	11	0	30	4	15	4	2325	593	3439	8	8
1194	104253	26	382	45	0	59	21	68	60	31747	6562	30553	41	41
1424	151090	57	435	89	0	73	27	102	74	32665	8208	24751	25	24
1386	147990	68	348	72	1	40	26	93	55	19249	7488	34458	22	22
839	87448	42	227	27	1	36	12	46	24	15292	4574	24649	18	18
596	27676	22	194	59	0	2	16	59	17	5842	522	2342	3	1
1684	170326	52	413	127	0	103	29	116	105	33994	12840	52739	54	53
1168	132148	38	273	48	1	30	26	29	20	13018	1350	6245	6	6
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1106	95778	34	343	58	0	46	25	91	51	98177	10623	35381	50	49
1149	109001	68	376	57	0	25	21	76	76	37941	5322	19595	33	33
1485	158833	46	495	60	0	59	24	86	61	31032	7987	50848	54	50
1529	150013	66	448	77	1	60	21	84	70	32683	10566	39443	63	64
962	89887	63	313	71	0	36	21	65	38	34545	1900	27023	56	53
78	3616	5	14	5	0	0	0	0	0	0	0	0	0	0
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1184	199005	45	410	70	0	45	23	84	81	27525	10698	61022	49	48
1672	160930	93	606	76	0	79	33	114	78	66856	14884	63528	90	90
2142	177948	102	593	124	2	30	32	132	76	28549	6852	34835	51	46
1016	136061	40	312	56	0	43	23	92	89	38610	6873	37172	29	29
778	43410	19	292	63	0	7	1	3	3	2781	4	13	1	1
1857	184277	75	547	92	1	80	29	109	87	41211	9188	62548	68	64
1084	109873	45	315	58	0	32	20	81	55	22698	5141	31334	29	29
2297	151030	59	660	64	8	84	33	121	73	41194	4260	20839	27	27
731	60493	40	174	29	3	3	12	48	32	32689	443	5084	4	4
285	19764	12	75	19	1	10	2	8	4	5752	2416	9927	10	10
1872	177559	56	572	64	3	47	21	80	70	26757	9831	53229	47	47
1181	140281	35	389	79	0	35	28	107	102	22527	5953	29877	44	44
1725	164249	54	562	104	0	54	35	140	109	44810	9435	37310	53	51
256	11796	9	79	22	0	1	2	8	1	0	0	0	0	0
98	10674	9	33	7	0	0	0	0	0	0	0	0	0	0
1435	151322	59	487	37	0	46	18	56	39	100674	7642	50067	40	38
41	6836	3	11	5	0	0	1	4	0	0	0	0	0	0
1931	174712	68	664	48	6	51	21	70	45	57786	6837	47708	57	57
42	5118	3	6	1	0	5	0	0	0	0	0	0	0	0
528	40248	16	183	34	1	8	4	14	7	5444	775	6012	6	6
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1122	127628	51	342	53	0	38	29	104	86	28470	8191	27749	24	22
1305	88837	38	269	44	0	21	26	89	52	61849	1661	47555	34	34
81	7131	4	27	0	1	0	0	0	0	0	0	0	0	0
262	9056	15	99	18	0	0	4	12	1	2179	548	1336	10	10
1104	88589	29	306	52	1	18	19	60	49	8019	3080	11017	16	16
1290	144470	53	327	56	0	53	22	84	72	39644	13400	55184	93	93
1248	111408	20	459	50	1	17	22	88	56	23494	8181	43485	28	22

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159782&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159782&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159782&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Goodness of Fit
Correlation	0.911
R-squared	0.8299
RMSE	373.5147

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.911 \tabularnewline
R-squared & 0.8299 \tabularnewline
RMSE & 373.5147 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159782&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.911[/C][/ROW]
[ROW][C]R-squared[/C][C]0.8299[/C][/ROW]
[ROW][C]RMSE[/C][C]373.5147[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159782&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159782&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Goodness of Fit
Correlation	0.911
R-squared	0.8299
RMSE	373.5147

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	1845	2048.4375	-203.4375
2	1797	1830.18181818182	-33.1818181818182
3	192	232.210526315789	-40.2105263157895
4	2444	2048.4375	395.5625
5	3567	3407.9	159.1
6	6953	3407.9	3545.1
7	1873	1830.18181818182	42.8181818181818
8	1740	1830.18181818182	-90.1818181818182
9	2079	1830.18181818182	248.818181818182
10	3120	3407.9	-287.9
11	1946	2048.4375	-102.4375
12	2370	2445.17391304348	-75.1739130434785
13	1962	2048.4375	-86.4375
14	3198	3407.9	-209.9
15	1496	1371.45454545455	124.545454545455
16	1574	1601.66666666667	-27.6666666666667
17	1808	1601.66666666667	206.333333333333
18	1309	1175.11538461538	133.884615384615
19	2820	2445.17391304348	374.826086956522
20	799	904.375	-105.375
21	1162	1175.11538461538	-13.1153846153845
22	2818	2445.17391304348	372.826086956522
23	1780	2048.4375	-268.4375
24	2316	2445.17391304348	-129.173913043478
25	1994	2048.4375	-54.4375
26	1806	1830.18181818182	-24.1818181818182
27	2153	2048.4375	104.5625
28	1458	1175.11538461538	282.884615384615
29	3000	3407.9	-407.9
30	2236	2445.17391304348	-209.173913043478
31	1685	1601.66666666667	83.3333333333333
32	1667	1830.18181818182	-163.181818181818
33	2257	2445.17391304348	-188.173913043478
34	3402	3407.9	-5.90000000000009
35	2571	2445.17391304348	125.826086956522
36	1	232.210526315789	-231.210526315789
37	2143	3407.9	-1264.9
38	1878	1830.18181818182	47.8181818181818
39	2224	1830.18181818182	393.818181818182
40	2186	2445.17391304348	-259.173913043478
41	2533	2445.17391304348	87.8260869565215
42	1823	1830.18181818182	-7.18181818181824
43	1095	1175.11538461538	-80.1153846153845
44	2303	2445.17391304348	-142.173913043478
45	1365	1371.45454545455	-6.4545454545455
46	1245	1175.11538461538	69.8846153846155
47	756	904.375	-148.375
48	2419	2445.17391304348	-26.1739130434785
49	2327	2445.17391304348	-118.173913043478
50	2787	3407.9	-620.9
51	658	232.210526315789	425.789473684211
52	2013	2048.4375	-35.4375
53	2616	2445.17391304348	170.826086956522
54	2072	2048.4375	23.5625
55	1912	1830.18181818182	81.8181818181818
56	1775	1830.18181818182	-55.1818181818182
57	1943	2048.4375	-105.4375
58	1047	1175.11538461538	-128.115384615385
59	1190	1371.45454545455	-181.454545454545
60	2932	3407.9	-475.9
61	1868	1830.18181818182	37.8181818181818
62	2255	2445.17391304348	-190.173913043478
63	1392	1175.11538461538	216.884615384615
64	1355	1371.45454545455	-16.4545454545455
65	1321	1371.45454545455	-50.4545454545455
66	1526	1371.45454545455	154.545454545455
67	2335	2048.4375	286.5625
68	2898	2445.17391304348	452.826086956522
69	1118	1175.11538461538	-57.1153846153845
70	340	232.210526315789	107.789473684211
71	2977	3407.9	-430.9
72	1452	1601.66666666667	-149.666666666667
73	1550	1830.18181818182	-280.181818181818
74	1685	1601.66666666667	83.3333333333333
75	2728	2445.17391304348	282.826086956522
76	1574	1601.66666666667	-27.6666666666667
77	2413	2445.17391304348	-32.1739130434785
78	2563	2445.17391304348	117.826086956522
79	1080	1175.11538461538	-95.1153846153845
80	1235	904.375	330.625
81	980	1175.11538461538	-195.115384615385
82	2246	2445.17391304348	-199.173913043478
83	1076	904.375	171.625
84	1638	1830.18181818182	-192.181818181818
85	1208	1175.11538461538	32.8846153846155
86	1866	1830.18181818182	35.8181818181818
87	2727	2048.4375	678.5625
88	1208	1175.11538461538	32.8846153846155
89	1427	1371.45454545455	55.5454545454545
90	1610	1830.18181818182	-220.181818181818
91	1865	1830.18181818182	34.8181818181818
92	2413	2445.17391304348	-32.1739130434785
93	1238	1371.45454545455	-133.454545454545
94	1468	1830.18181818182	-362.181818181818
95	974	904.375	69.625
96	2319	2445.17391304348	-126.173913043478
97	1890	2048.4375	-158.4375
98	223	232.210526315789	-9.21052631578948
99	2527	2445.17391304348	81.8260869565215
100	2105	2445.17391304348	-340.173913043478
101	778	904.375	-126.375
102	1194	1175.11538461538	18.8846153846155
103	1424	1601.66666666667	-177.666666666667
104	1386	1175.11538461538	210.884615384615
105	839	904.375	-65.375
106	596	232.210526315789	363.789473684211
107	1684	1601.66666666667	82.3333333333333
108	1168	1175.11538461538	-7.11538461538453
109	0	232.210526315789	-232.210526315789
110	1106	1175.11538461538	-69.1153846153845
111	1149	1175.11538461538	-26.1153846153845
112	1485	1371.45454545455	113.545454545455
113	1529	1601.66666666667	-72.6666666666667
114	962	1175.11538461538	-213.115384615385
115	78	232.210526315789	-154.210526315789
116	0	232.210526315789	-232.210526315789
117	1184	1175.11538461538	8.88461538461547
118	1672	2048.4375	-376.4375
119	2142	2048.4375	93.5625
120	1016	1175.11538461538	-159.115384615385
121	778	904.375	-126.375
122	1857	2048.4375	-191.4375
123	1084	1175.11538461538	-91.1153846153845
124	2297	1830.18181818182	466.818181818182
125	731	232.210526315789	498.789473684211
126	285	232.210526315789	52.7894736842105
127	1872	1830.18181818182	41.8181818181818
128	1181	1175.11538461538	5.88461538461547
129	1725	1830.18181818182	-105.181818181818
130	256	232.210526315789	23.7894736842105
131	98	232.210526315789	-134.210526315789
132	1435	1371.45454545455	63.5454545454545
133	41	232.210526315789	-191.210526315789
134	1931	1830.18181818182	100.818181818182
135	42	232.210526315789	-190.210526315789
136	528	232.210526315789	295.789473684211
137	0	232.210526315789	-232.210526315789
138	1122	1175.11538461538	-53.1153846153845
139	1305	1175.11538461538	129.884615384615
140	81	232.210526315789	-151.210526315789
141	262	232.210526315789	29.7894736842105
142	1104	1175.11538461538	-71.1153846153845
143	1290	1175.11538461538	114.884615384615
144	1248	1371.45454545455	-123.454545454545

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 1845 & 2048.4375 & -203.4375 \tabularnewline
2 & 1797 & 1830.18181818182 & -33.1818181818182 \tabularnewline
3 & 192 & 232.210526315789 & -40.2105263157895 \tabularnewline
4 & 2444 & 2048.4375 & 395.5625 \tabularnewline
5 & 3567 & 3407.9 & 159.1 \tabularnewline
6 & 6953 & 3407.9 & 3545.1 \tabularnewline
7 & 1873 & 1830.18181818182 & 42.8181818181818 \tabularnewline
8 & 1740 & 1830.18181818182 & -90.1818181818182 \tabularnewline
9 & 2079 & 1830.18181818182 & 248.818181818182 \tabularnewline
10 & 3120 & 3407.9 & -287.9 \tabularnewline
11 & 1946 & 2048.4375 & -102.4375 \tabularnewline
12 & 2370 & 2445.17391304348 & -75.1739130434785 \tabularnewline
13 & 1962 & 2048.4375 & -86.4375 \tabularnewline
14 & 3198 & 3407.9 & -209.9 \tabularnewline
15 & 1496 & 1371.45454545455 & 124.545454545455 \tabularnewline
16 & 1574 & 1601.66666666667 & -27.6666666666667 \tabularnewline
17 & 1808 & 1601.66666666667 & 206.333333333333 \tabularnewline
18 & 1309 & 1175.11538461538 & 133.884615384615 \tabularnewline
19 & 2820 & 2445.17391304348 & 374.826086956522 \tabularnewline
20 & 799 & 904.375 & -105.375 \tabularnewline
21 & 1162 & 1175.11538461538 & -13.1153846153845 \tabularnewline
22 & 2818 & 2445.17391304348 & 372.826086956522 \tabularnewline
23 & 1780 & 2048.4375 & -268.4375 \tabularnewline
24 & 2316 & 2445.17391304348 & -129.173913043478 \tabularnewline
25 & 1994 & 2048.4375 & -54.4375 \tabularnewline
26 & 1806 & 1830.18181818182 & -24.1818181818182 \tabularnewline
27 & 2153 & 2048.4375 & 104.5625 \tabularnewline
28 & 1458 & 1175.11538461538 & 282.884615384615 \tabularnewline
29 & 3000 & 3407.9 & -407.9 \tabularnewline
30 & 2236 & 2445.17391304348 & -209.173913043478 \tabularnewline
31 & 1685 & 1601.66666666667 & 83.3333333333333 \tabularnewline
32 & 1667 & 1830.18181818182 & -163.181818181818 \tabularnewline
33 & 2257 & 2445.17391304348 & -188.173913043478 \tabularnewline
34 & 3402 & 3407.9 & -5.90000000000009 \tabularnewline
35 & 2571 & 2445.17391304348 & 125.826086956522 \tabularnewline
36 & 1 & 232.210526315789 & -231.210526315789 \tabularnewline
37 & 2143 & 3407.9 & -1264.9 \tabularnewline
38 & 1878 & 1830.18181818182 & 47.8181818181818 \tabularnewline
39 & 2224 & 1830.18181818182 & 393.818181818182 \tabularnewline
40 & 2186 & 2445.17391304348 & -259.173913043478 \tabularnewline
41 & 2533 & 2445.17391304348 & 87.8260869565215 \tabularnewline
42 & 1823 & 1830.18181818182 & -7.18181818181824 \tabularnewline
43 & 1095 & 1175.11538461538 & -80.1153846153845 \tabularnewline
44 & 2303 & 2445.17391304348 & -142.173913043478 \tabularnewline
45 & 1365 & 1371.45454545455 & -6.4545454545455 \tabularnewline
46 & 1245 & 1175.11538461538 & 69.8846153846155 \tabularnewline
47 & 756 & 904.375 & -148.375 \tabularnewline
48 & 2419 & 2445.17391304348 & -26.1739130434785 \tabularnewline
49 & 2327 & 2445.17391304348 & -118.173913043478 \tabularnewline
50 & 2787 & 3407.9 & -620.9 \tabularnewline
51 & 658 & 232.210526315789 & 425.789473684211 \tabularnewline
52 & 2013 & 2048.4375 & -35.4375 \tabularnewline
53 & 2616 & 2445.17391304348 & 170.826086956522 \tabularnewline
54 & 2072 & 2048.4375 & 23.5625 \tabularnewline
55 & 1912 & 1830.18181818182 & 81.8181818181818 \tabularnewline
56 & 1775 & 1830.18181818182 & -55.1818181818182 \tabularnewline
57 & 1943 & 2048.4375 & -105.4375 \tabularnewline
58 & 1047 & 1175.11538461538 & -128.115384615385 \tabularnewline
59 & 1190 & 1371.45454545455 & -181.454545454545 \tabularnewline
60 & 2932 & 3407.9 & -475.9 \tabularnewline
61 & 1868 & 1830.18181818182 & 37.8181818181818 \tabularnewline
62 & 2255 & 2445.17391304348 & -190.173913043478 \tabularnewline
63 & 1392 & 1175.11538461538 & 216.884615384615 \tabularnewline
64 & 1355 & 1371.45454545455 & -16.4545454545455 \tabularnewline
65 & 1321 & 1371.45454545455 & -50.4545454545455 \tabularnewline
66 & 1526 & 1371.45454545455 & 154.545454545455 \tabularnewline
67 & 2335 & 2048.4375 & 286.5625 \tabularnewline
68 & 2898 & 2445.17391304348 & 452.826086956522 \tabularnewline
69 & 1118 & 1175.11538461538 & -57.1153846153845 \tabularnewline
70 & 340 & 232.210526315789 & 107.789473684211 \tabularnewline
71 & 2977 & 3407.9 & -430.9 \tabularnewline
72 & 1452 & 1601.66666666667 & -149.666666666667 \tabularnewline
73 & 1550 & 1830.18181818182 & -280.181818181818 \tabularnewline
74 & 1685 & 1601.66666666667 & 83.3333333333333 \tabularnewline
75 & 2728 & 2445.17391304348 & 282.826086956522 \tabularnewline
76 & 1574 & 1601.66666666667 & -27.6666666666667 \tabularnewline
77 & 2413 & 2445.17391304348 & -32.1739130434785 \tabularnewline
78 & 2563 & 2445.17391304348 & 117.826086956522 \tabularnewline
79 & 1080 & 1175.11538461538 & -95.1153846153845 \tabularnewline
80 & 1235 & 904.375 & 330.625 \tabularnewline
81 & 980 & 1175.11538461538 & -195.115384615385 \tabularnewline
82 & 2246 & 2445.17391304348 & -199.173913043478 \tabularnewline
83 & 1076 & 904.375 & 171.625 \tabularnewline
84 & 1638 & 1830.18181818182 & -192.181818181818 \tabularnewline
85 & 1208 & 1175.11538461538 & 32.8846153846155 \tabularnewline
86 & 1866 & 1830.18181818182 & 35.8181818181818 \tabularnewline
87 & 2727 & 2048.4375 & 678.5625 \tabularnewline
88 & 1208 & 1175.11538461538 & 32.8846153846155 \tabularnewline
89 & 1427 & 1371.45454545455 & 55.5454545454545 \tabularnewline
90 & 1610 & 1830.18181818182 & -220.181818181818 \tabularnewline
91 & 1865 & 1830.18181818182 & 34.8181818181818 \tabularnewline
92 & 2413 & 2445.17391304348 & -32.1739130434785 \tabularnewline
93 & 1238 & 1371.45454545455 & -133.454545454545 \tabularnewline
94 & 1468 & 1830.18181818182 & -362.181818181818 \tabularnewline
95 & 974 & 904.375 & 69.625 \tabularnewline
96 & 2319 & 2445.17391304348 & -126.173913043478 \tabularnewline
97 & 1890 & 2048.4375 & -158.4375 \tabularnewline
98 & 223 & 232.210526315789 & -9.21052631578948 \tabularnewline
99 & 2527 & 2445.17391304348 & 81.8260869565215 \tabularnewline
100 & 2105 & 2445.17391304348 & -340.173913043478 \tabularnewline
101 & 778 & 904.375 & -126.375 \tabularnewline
102 & 1194 & 1175.11538461538 & 18.8846153846155 \tabularnewline
103 & 1424 & 1601.66666666667 & -177.666666666667 \tabularnewline
104 & 1386 & 1175.11538461538 & 210.884615384615 \tabularnewline
105 & 839 & 904.375 & -65.375 \tabularnewline
106 & 596 & 232.210526315789 & 363.789473684211 \tabularnewline
107 & 1684 & 1601.66666666667 & 82.3333333333333 \tabularnewline
108 & 1168 & 1175.11538461538 & -7.11538461538453 \tabularnewline
109 & 0 & 232.210526315789 & -232.210526315789 \tabularnewline
110 & 1106 & 1175.11538461538 & -69.1153846153845 \tabularnewline
111 & 1149 & 1175.11538461538 & -26.1153846153845 \tabularnewline
112 & 1485 & 1371.45454545455 & 113.545454545455 \tabularnewline
113 & 1529 & 1601.66666666667 & -72.6666666666667 \tabularnewline
114 & 962 & 1175.11538461538 & -213.115384615385 \tabularnewline
115 & 78 & 232.210526315789 & -154.210526315789 \tabularnewline
116 & 0 & 232.210526315789 & -232.210526315789 \tabularnewline
117 & 1184 & 1175.11538461538 & 8.88461538461547 \tabularnewline
118 & 1672 & 2048.4375 & -376.4375 \tabularnewline
119 & 2142 & 2048.4375 & 93.5625 \tabularnewline
120 & 1016 & 1175.11538461538 & -159.115384615385 \tabularnewline
121 & 778 & 904.375 & -126.375 \tabularnewline
122 & 1857 & 2048.4375 & -191.4375 \tabularnewline
123 & 1084 & 1175.11538461538 & -91.1153846153845 \tabularnewline
124 & 2297 & 1830.18181818182 & 466.818181818182 \tabularnewline
125 & 731 & 232.210526315789 & 498.789473684211 \tabularnewline
126 & 285 & 232.210526315789 & 52.7894736842105 \tabularnewline
127 & 1872 & 1830.18181818182 & 41.8181818181818 \tabularnewline
128 & 1181 & 1175.11538461538 & 5.88461538461547 \tabularnewline
129 & 1725 & 1830.18181818182 & -105.181818181818 \tabularnewline
130 & 256 & 232.210526315789 & 23.7894736842105 \tabularnewline
131 & 98 & 232.210526315789 & -134.210526315789 \tabularnewline
132 & 1435 & 1371.45454545455 & 63.5454545454545 \tabularnewline
133 & 41 & 232.210526315789 & -191.210526315789 \tabularnewline
134 & 1931 & 1830.18181818182 & 100.818181818182 \tabularnewline
135 & 42 & 232.210526315789 & -190.210526315789 \tabularnewline
136 & 528 & 232.210526315789 & 295.789473684211 \tabularnewline
137 & 0 & 232.210526315789 & -232.210526315789 \tabularnewline
138 & 1122 & 1175.11538461538 & -53.1153846153845 \tabularnewline
139 & 1305 & 1175.11538461538 & 129.884615384615 \tabularnewline
140 & 81 & 232.210526315789 & -151.210526315789 \tabularnewline
141 & 262 & 232.210526315789 & 29.7894736842105 \tabularnewline
142 & 1104 & 1175.11538461538 & -71.1153846153845 \tabularnewline
143 & 1290 & 1175.11538461538 & 114.884615384615 \tabularnewline
144 & 1248 & 1371.45454545455 & -123.454545454545 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159782&T=2

[TABLE]
[ROW][C]Actuals, Predictions, and Residuals[/C][/ROW]
[ROW][C]#[/C][C]Actuals[/C][C]Forecasts[/C][C]Residuals[/C][/ROW]
[ROW][C]1[/C][C]1845[/C][C]2048.4375[/C][C]-203.4375[/C][/ROW]
[ROW][C]2[/C][C]1797[/C][C]1830.18181818182[/C][C]-33.1818181818182[/C][/ROW]
[ROW][C]3[/C][C]192[/C][C]232.210526315789[/C][C]-40.2105263157895[/C][/ROW]
[ROW][C]4[/C][C]2444[/C][C]2048.4375[/C][C]395.5625[/C][/ROW]
[ROW][C]5[/C][C]3567[/C][C]3407.9[/C][C]159.1[/C][/ROW]
[ROW][C]6[/C][C]6953[/C][C]3407.9[/C][C]3545.1[/C][/ROW]
[ROW][C]7[/C][C]1873[/C][C]1830.18181818182[/C][C]42.8181818181818[/C][/ROW]
[ROW][C]8[/C][C]1740[/C][C]1830.18181818182[/C][C]-90.1818181818182[/C][/ROW]
[ROW][C]9[/C][C]2079[/C][C]1830.18181818182[/C][C]248.818181818182[/C][/ROW]
[ROW][C]10[/C][C]3120[/C][C]3407.9[/C][C]-287.9[/C][/ROW]
[ROW][C]11[/C][C]1946[/C][C]2048.4375[/C][C]-102.4375[/C][/ROW]
[ROW][C]12[/C][C]2370[/C][C]2445.17391304348[/C][C]-75.1739130434785[/C][/ROW]
[ROW][C]13[/C][C]1962[/C][C]2048.4375[/C][C]-86.4375[/C][/ROW]
[ROW][C]14[/C][C]3198[/C][C]3407.9[/C][C]-209.9[/C][/ROW]
[ROW][C]15[/C][C]1496[/C][C]1371.45454545455[/C][C]124.545454545455[/C][/ROW]
[ROW][C]16[/C][C]1574[/C][C]1601.66666666667[/C][C]-27.6666666666667[/C][/ROW]
[ROW][C]17[/C][C]1808[/C][C]1601.66666666667[/C][C]206.333333333333[/C][/ROW]
[ROW][C]18[/C][C]1309[/C][C]1175.11538461538[/C][C]133.884615384615[/C][/ROW]
[ROW][C]19[/C][C]2820[/C][C]2445.17391304348[/C][C]374.826086956522[/C][/ROW]
[ROW][C]20[/C][C]799[/C][C]904.375[/C][C]-105.375[/C][/ROW]
[ROW][C]21[/C][C]1162[/C][C]1175.11538461538[/C][C]-13.1153846153845[/C][/ROW]
[ROW][C]22[/C][C]2818[/C][C]2445.17391304348[/C][C]372.826086956522[/C][/ROW]
[ROW][C]23[/C][C]1780[/C][C]2048.4375[/C][C]-268.4375[/C][/ROW]
[ROW][C]24[/C][C]2316[/C][C]2445.17391304348[/C][C]-129.173913043478[/C][/ROW]
[ROW][C]25[/C][C]1994[/C][C]2048.4375[/C][C]-54.4375[/C][/ROW]
[ROW][C]26[/C][C]1806[/C][C]1830.18181818182[/C][C]-24.1818181818182[/C][/ROW]
[ROW][C]27[/C][C]2153[/C][C]2048.4375[/C][C]104.5625[/C][/ROW]
[ROW][C]28[/C][C]1458[/C][C]1175.11538461538[/C][C]282.884615384615[/C][/ROW]
[ROW][C]29[/C][C]3000[/C][C]3407.9[/C][C]-407.9[/C][/ROW]
[ROW][C]30[/C][C]2236[/C][C]2445.17391304348[/C][C]-209.173913043478[/C][/ROW]
[ROW][C]31[/C][C]1685[/C][C]1601.66666666667[/C][C]83.3333333333333[/C][/ROW]
[ROW][C]32[/C][C]1667[/C][C]1830.18181818182[/C][C]-163.181818181818[/C][/ROW]
[ROW][C]33[/C][C]2257[/C][C]2445.17391304348[/C][C]-188.173913043478[/C][/ROW]
[ROW][C]34[/C][C]3402[/C][C]3407.9[/C][C]-5.90000000000009[/C][/ROW]
[ROW][C]35[/C][C]2571[/C][C]2445.17391304348[/C][C]125.826086956522[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]232.210526315789[/C][C]-231.210526315789[/C][/ROW]
[ROW][C]37[/C][C]2143[/C][C]3407.9[/C][C]-1264.9[/C][/ROW]
[ROW][C]38[/C][C]1878[/C][C]1830.18181818182[/C][C]47.8181818181818[/C][/ROW]
[ROW][C]39[/C][C]2224[/C][C]1830.18181818182[/C][C]393.818181818182[/C][/ROW]
[ROW][C]40[/C][C]2186[/C][C]2445.17391304348[/C][C]-259.173913043478[/C][/ROW]
[ROW][C]41[/C][C]2533[/C][C]2445.17391304348[/C][C]87.8260869565215[/C][/ROW]
[ROW][C]42[/C][C]1823[/C][C]1830.18181818182[/C][C]-7.18181818181824[/C][/ROW]
[ROW][C]43[/C][C]1095[/C][C]1175.11538461538[/C][C]-80.1153846153845[/C][/ROW]
[ROW][C]44[/C][C]2303[/C][C]2445.17391304348[/C][C]-142.173913043478[/C][/ROW]
[ROW][C]45[/C][C]1365[/C][C]1371.45454545455[/C][C]-6.4545454545455[/C][/ROW]
[ROW][C]46[/C][C]1245[/C][C]1175.11538461538[/C][C]69.8846153846155[/C][/ROW]
[ROW][C]47[/C][C]756[/C][C]904.375[/C][C]-148.375[/C][/ROW]
[ROW][C]48[/C][C]2419[/C][C]2445.17391304348[/C][C]-26.1739130434785[/C][/ROW]
[ROW][C]49[/C][C]2327[/C][C]2445.17391304348[/C][C]-118.173913043478[/C][/ROW]
[ROW][C]50[/C][C]2787[/C][C]3407.9[/C][C]-620.9[/C][/ROW]
[ROW][C]51[/C][C]658[/C][C]232.210526315789[/C][C]425.789473684211[/C][/ROW]
[ROW][C]52[/C][C]2013[/C][C]2048.4375[/C][C]-35.4375[/C][/ROW]
[ROW][C]53[/C][C]2616[/C][C]2445.17391304348[/C][C]170.826086956522[/C][/ROW]
[ROW][C]54[/C][C]2072[/C][C]2048.4375[/C][C]23.5625[/C][/ROW]
[ROW][C]55[/C][C]1912[/C][C]1830.18181818182[/C][C]81.8181818181818[/C][/ROW]
[ROW][C]56[/C][C]1775[/C][C]1830.18181818182[/C][C]-55.1818181818182[/C][/ROW]
[ROW][C]57[/C][C]1943[/C][C]2048.4375[/C][C]-105.4375[/C][/ROW]
[ROW][C]58[/C][C]1047[/C][C]1175.11538461538[/C][C]-128.115384615385[/C][/ROW]
[ROW][C]59[/C][C]1190[/C][C]1371.45454545455[/C][C]-181.454545454545[/C][/ROW]
[ROW][C]60[/C][C]2932[/C][C]3407.9[/C][C]-475.9[/C][/ROW]
[ROW][C]61[/C][C]1868[/C][C]1830.18181818182[/C][C]37.8181818181818[/C][/ROW]
[ROW][C]62[/C][C]2255[/C][C]2445.17391304348[/C][C]-190.173913043478[/C][/ROW]
[ROW][C]63[/C][C]1392[/C][C]1175.11538461538[/C][C]216.884615384615[/C][/ROW]
[ROW][C]64[/C][C]1355[/C][C]1371.45454545455[/C][C]-16.4545454545455[/C][/ROW]
[ROW][C]65[/C][C]1321[/C][C]1371.45454545455[/C][C]-50.4545454545455[/C][/ROW]
[ROW][C]66[/C][C]1526[/C][C]1371.45454545455[/C][C]154.545454545455[/C][/ROW]
[ROW][C]67[/C][C]2335[/C][C]2048.4375[/C][C]286.5625[/C][/ROW]
[ROW][C]68[/C][C]2898[/C][C]2445.17391304348[/C][C]452.826086956522[/C][/ROW]
[ROW][C]69[/C][C]1118[/C][C]1175.11538461538[/C][C]-57.1153846153845[/C][/ROW]
[ROW][C]70[/C][C]340[/C][C]232.210526315789[/C][C]107.789473684211[/C][/ROW]
[ROW][C]71[/C][C]2977[/C][C]3407.9[/C][C]-430.9[/C][/ROW]
[ROW][C]72[/C][C]1452[/C][C]1601.66666666667[/C][C]-149.666666666667[/C][/ROW]
[ROW][C]73[/C][C]1550[/C][C]1830.18181818182[/C][C]-280.181818181818[/C][/ROW]
[ROW][C]74[/C][C]1685[/C][C]1601.66666666667[/C][C]83.3333333333333[/C][/ROW]
[ROW][C]75[/C][C]2728[/C][C]2445.17391304348[/C][C]282.826086956522[/C][/ROW]
[ROW][C]76[/C][C]1574[/C][C]1601.66666666667[/C][C]-27.6666666666667[/C][/ROW]
[ROW][C]77[/C][C]2413[/C][C]2445.17391304348[/C][C]-32.1739130434785[/C][/ROW]
[ROW][C]78[/C][C]2563[/C][C]2445.17391304348[/C][C]117.826086956522[/C][/ROW]
[ROW][C]79[/C][C]1080[/C][C]1175.11538461538[/C][C]-95.1153846153845[/C][/ROW]
[ROW][C]80[/C][C]1235[/C][C]904.375[/C][C]330.625[/C][/ROW]
[ROW][C]81[/C][C]980[/C][C]1175.11538461538[/C][C]-195.115384615385[/C][/ROW]
[ROW][C]82[/C][C]2246[/C][C]2445.17391304348[/C][C]-199.173913043478[/C][/ROW]
[ROW][C]83[/C][C]1076[/C][C]904.375[/C][C]171.625[/C][/ROW]
[ROW][C]84[/C][C]1638[/C][C]1830.18181818182[/C][C]-192.181818181818[/C][/ROW]
[ROW][C]85[/C][C]1208[/C][C]1175.11538461538[/C][C]32.8846153846155[/C][/ROW]
[ROW][C]86[/C][C]1866[/C][C]1830.18181818182[/C][C]35.8181818181818[/C][/ROW]
[ROW][C]87[/C][C]2727[/C][C]2048.4375[/C][C]678.5625[/C][/ROW]
[ROW][C]88[/C][C]1208[/C][C]1175.11538461538[/C][C]32.8846153846155[/C][/ROW]
[ROW][C]89[/C][C]1427[/C][C]1371.45454545455[/C][C]55.5454545454545[/C][/ROW]
[ROW][C]90[/C][C]1610[/C][C]1830.18181818182[/C][C]-220.181818181818[/C][/ROW]
[ROW][C]91[/C][C]1865[/C][C]1830.18181818182[/C][C]34.8181818181818[/C][/ROW]
[ROW][C]92[/C][C]2413[/C][C]2445.17391304348[/C][C]-32.1739130434785[/C][/ROW]
[ROW][C]93[/C][C]1238[/C][C]1371.45454545455[/C][C]-133.454545454545[/C][/ROW]
[ROW][C]94[/C][C]1468[/C][C]1830.18181818182[/C][C]-362.181818181818[/C][/ROW]
[ROW][C]95[/C][C]974[/C][C]904.375[/C][C]69.625[/C][/ROW]
[ROW][C]96[/C][C]2319[/C][C]2445.17391304348[/C][C]-126.173913043478[/C][/ROW]
[ROW][C]97[/C][C]1890[/C][C]2048.4375[/C][C]-158.4375[/C][/ROW]
[ROW][C]98[/C][C]223[/C][C]232.210526315789[/C][C]-9.21052631578948[/C][/ROW]
[ROW][C]99[/C][C]2527[/C][C]2445.17391304348[/C][C]81.8260869565215[/C][/ROW]
[ROW][C]100[/C][C]2105[/C][C]2445.17391304348[/C][C]-340.173913043478[/C][/ROW]
[ROW][C]101[/C][C]778[/C][C]904.375[/C][C]-126.375[/C][/ROW]
[ROW][C]102[/C][C]1194[/C][C]1175.11538461538[/C][C]18.8846153846155[/C][/ROW]
[ROW][C]103[/C][C]1424[/C][C]1601.66666666667[/C][C]-177.666666666667[/C][/ROW]
[ROW][C]104[/C][C]1386[/C][C]1175.11538461538[/C][C]210.884615384615[/C][/ROW]
[ROW][C]105[/C][C]839[/C][C]904.375[/C][C]-65.375[/C][/ROW]
[ROW][C]106[/C][C]596[/C][C]232.210526315789[/C][C]363.789473684211[/C][/ROW]
[ROW][C]107[/C][C]1684[/C][C]1601.66666666667[/C][C]82.3333333333333[/C][/ROW]
[ROW][C]108[/C][C]1168[/C][C]1175.11538461538[/C][C]-7.11538461538453[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]232.210526315789[/C][C]-232.210526315789[/C][/ROW]
[ROW][C]110[/C][C]1106[/C][C]1175.11538461538[/C][C]-69.1153846153845[/C][/ROW]
[ROW][C]111[/C][C]1149[/C][C]1175.11538461538[/C][C]-26.1153846153845[/C][/ROW]
[ROW][C]112[/C][C]1485[/C][C]1371.45454545455[/C][C]113.545454545455[/C][/ROW]
[ROW][C]113[/C][C]1529[/C][C]1601.66666666667[/C][C]-72.6666666666667[/C][/ROW]
[ROW][C]114[/C][C]962[/C][C]1175.11538461538[/C][C]-213.115384615385[/C][/ROW]
[ROW][C]115[/C][C]78[/C][C]232.210526315789[/C][C]-154.210526315789[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]232.210526315789[/C][C]-232.210526315789[/C][/ROW]
[ROW][C]117[/C][C]1184[/C][C]1175.11538461538[/C][C]8.88461538461547[/C][/ROW]
[ROW][C]118[/C][C]1672[/C][C]2048.4375[/C][C]-376.4375[/C][/ROW]
[ROW][C]119[/C][C]2142[/C][C]2048.4375[/C][C]93.5625[/C][/ROW]
[ROW][C]120[/C][C]1016[/C][C]1175.11538461538[/C][C]-159.115384615385[/C][/ROW]
[ROW][C]121[/C][C]778[/C][C]904.375[/C][C]-126.375[/C][/ROW]
[ROW][C]122[/C][C]1857[/C][C]2048.4375[/C][C]-191.4375[/C][/ROW]
[ROW][C]123[/C][C]1084[/C][C]1175.11538461538[/C][C]-91.1153846153845[/C][/ROW]
[ROW][C]124[/C][C]2297[/C][C]1830.18181818182[/C][C]466.818181818182[/C][/ROW]
[ROW][C]125[/C][C]731[/C][C]232.210526315789[/C][C]498.789473684211[/C][/ROW]
[ROW][C]126[/C][C]285[/C][C]232.210526315789[/C][C]52.7894736842105[/C][/ROW]
[ROW][C]127[/C][C]1872[/C][C]1830.18181818182[/C][C]41.8181818181818[/C][/ROW]
[ROW][C]128[/C][C]1181[/C][C]1175.11538461538[/C][C]5.88461538461547[/C][/ROW]
[ROW][C]129[/C][C]1725[/C][C]1830.18181818182[/C][C]-105.181818181818[/C][/ROW]
[ROW][C]130[/C][C]256[/C][C]232.210526315789[/C][C]23.7894736842105[/C][/ROW]
[ROW][C]131[/C][C]98[/C][C]232.210526315789[/C][C]-134.210526315789[/C][/ROW]
[ROW][C]132[/C][C]1435[/C][C]1371.45454545455[/C][C]63.5454545454545[/C][/ROW]
[ROW][C]133[/C][C]41[/C][C]232.210526315789[/C][C]-191.210526315789[/C][/ROW]
[ROW][C]134[/C][C]1931[/C][C]1830.18181818182[/C][C]100.818181818182[/C][/ROW]
[ROW][C]135[/C][C]42[/C][C]232.210526315789[/C][C]-190.210526315789[/C][/ROW]
[ROW][C]136[/C][C]528[/C][C]232.210526315789[/C][C]295.789473684211[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]232.210526315789[/C][C]-232.210526315789[/C][/ROW]
[ROW][C]138[/C][C]1122[/C][C]1175.11538461538[/C][C]-53.1153846153845[/C][/ROW]
[ROW][C]139[/C][C]1305[/C][C]1175.11538461538[/C][C]129.884615384615[/C][/ROW]
[ROW][C]140[/C][C]81[/C][C]232.210526315789[/C][C]-151.210526315789[/C][/ROW]
[ROW][C]141[/C][C]262[/C][C]232.210526315789[/C][C]29.7894736842105[/C][/ROW]
[ROW][C]142[/C][C]1104[/C][C]1175.11538461538[/C][C]-71.1153846153845[/C][/ROW]
[ROW][C]143[/C][C]1290[/C][C]1175.11538461538[/C][C]114.884615384615[/C][/ROW]
[ROW][C]144[/C][C]1248[/C][C]1371.45454545455[/C][C]-123.454545454545[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159782&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159782&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	1845	2048.4375	-203.4375
2	1797	1830.18181818182	-33.1818181818182
3	192	232.210526315789	-40.2105263157895
4	2444	2048.4375	395.5625
5	3567	3407.9	159.1
6	6953	3407.9	3545.1
7	1873	1830.18181818182	42.8181818181818
8	1740	1830.18181818182	-90.1818181818182
9	2079	1830.18181818182	248.818181818182
10	3120	3407.9	-287.9
11	1946	2048.4375	-102.4375
12	2370	2445.17391304348	-75.1739130434785
13	1962	2048.4375	-86.4375
14	3198	3407.9	-209.9
15	1496	1371.45454545455	124.545454545455
16	1574	1601.66666666667	-27.6666666666667
17	1808	1601.66666666667	206.333333333333
18	1309	1175.11538461538	133.884615384615
19	2820	2445.17391304348	374.826086956522
20	799	904.375	-105.375
21	1162	1175.11538461538	-13.1153846153845
22	2818	2445.17391304348	372.826086956522
23	1780	2048.4375	-268.4375
24	2316	2445.17391304348	-129.173913043478
25	1994	2048.4375	-54.4375
26	1806	1830.18181818182	-24.1818181818182
27	2153	2048.4375	104.5625
28	1458	1175.11538461538	282.884615384615
29	3000	3407.9	-407.9
30	2236	2445.17391304348	-209.173913043478
31	1685	1601.66666666667	83.3333333333333
32	1667	1830.18181818182	-163.181818181818
33	2257	2445.17391304348	-188.173913043478
34	3402	3407.9	-5.90000000000009
35	2571	2445.17391304348	125.826086956522
36	1	232.210526315789	-231.210526315789
37	2143	3407.9	-1264.9
38	1878	1830.18181818182	47.8181818181818
39	2224	1830.18181818182	393.818181818182
40	2186	2445.17391304348	-259.173913043478
41	2533	2445.17391304348	87.8260869565215
42	1823	1830.18181818182	-7.18181818181824
43	1095	1175.11538461538	-80.1153846153845
44	2303	2445.17391304348	-142.173913043478
45	1365	1371.45454545455	-6.4545454545455
46	1245	1175.11538461538	69.8846153846155
47	756	904.375	-148.375
48	2419	2445.17391304348	-26.1739130434785
49	2327	2445.17391304348	-118.173913043478
50	2787	3407.9	-620.9
51	658	232.210526315789	425.789473684211
52	2013	2048.4375	-35.4375
53	2616	2445.17391304348	170.826086956522
54	2072	2048.4375	23.5625
55	1912	1830.18181818182	81.8181818181818
56	1775	1830.18181818182	-55.1818181818182
57	1943	2048.4375	-105.4375
58	1047	1175.11538461538	-128.115384615385
59	1190	1371.45454545455	-181.454545454545
60	2932	3407.9	-475.9
61	1868	1830.18181818182	37.8181818181818
62	2255	2445.17391304348	-190.173913043478
63	1392	1175.11538461538	216.884615384615
64	1355	1371.45454545455	-16.4545454545455
65	1321	1371.45454545455	-50.4545454545455
66	1526	1371.45454545455	154.545454545455
67	2335	2048.4375	286.5625
68	2898	2445.17391304348	452.826086956522
69	1118	1175.11538461538	-57.1153846153845
70	340	232.210526315789	107.789473684211
71	2977	3407.9	-430.9
72	1452	1601.66666666667	-149.666666666667
73	1550	1830.18181818182	-280.181818181818
74	1685	1601.66666666667	83.3333333333333
75	2728	2445.17391304348	282.826086956522
76	1574	1601.66666666667	-27.6666666666667
77	2413	2445.17391304348	-32.1739130434785
78	2563	2445.17391304348	117.826086956522
79	1080	1175.11538461538	-95.1153846153845
80	1235	904.375	330.625
81	980	1175.11538461538	-195.115384615385
82	2246	2445.17391304348	-199.173913043478
83	1076	904.375	171.625
84	1638	1830.18181818182	-192.181818181818
85	1208	1175.11538461538	32.8846153846155
86	1866	1830.18181818182	35.8181818181818
87	2727	2048.4375	678.5625
88	1208	1175.11538461538	32.8846153846155
89	1427	1371.45454545455	55.5454545454545
90	1610	1830.18181818182	-220.181818181818
91	1865	1830.18181818182	34.8181818181818
92	2413	2445.17391304348	-32.1739130434785
93	1238	1371.45454545455	-133.454545454545
94	1468	1830.18181818182	-362.181818181818
95	974	904.375	69.625
96	2319	2445.17391304348	-126.173913043478
97	1890	2048.4375	-158.4375
98	223	232.210526315789	-9.21052631578948
99	2527	2445.17391304348	81.8260869565215
100	2105	2445.17391304348	-340.173913043478
101	778	904.375	-126.375
102	1194	1175.11538461538	18.8846153846155
103	1424	1601.66666666667	-177.666666666667
104	1386	1175.11538461538	210.884615384615
105	839	904.375	-65.375
106	596	232.210526315789	363.789473684211
107	1684	1601.66666666667	82.3333333333333
108	1168	1175.11538461538	-7.11538461538453
109	0	232.210526315789	-232.210526315789
110	1106	1175.11538461538	-69.1153846153845
111	1149	1175.11538461538	-26.1153846153845
112	1485	1371.45454545455	113.545454545455
113	1529	1601.66666666667	-72.6666666666667
114	962	1175.11538461538	-213.115384615385
115	78	232.210526315789	-154.210526315789
116	0	232.210526315789	-232.210526315789
117	1184	1175.11538461538	8.88461538461547
118	1672	2048.4375	-376.4375
119	2142	2048.4375	93.5625
120	1016	1175.11538461538	-159.115384615385
121	778	904.375	-126.375
122	1857	2048.4375	-191.4375
123	1084	1175.11538461538	-91.1153846153845
124	2297	1830.18181818182	466.818181818182
125	731	232.210526315789	498.789473684211
126	285	232.210526315789	52.7894736842105
127	1872	1830.18181818182	41.8181818181818
128	1181	1175.11538461538	5.88461538461547
129	1725	1830.18181818182	-105.181818181818
130	256	232.210526315789	23.7894736842105
131	98	232.210526315789	-134.210526315789
132	1435	1371.45454545455	63.5454545454545
133	41	232.210526315789	-191.210526315789
134	1931	1830.18181818182	100.818181818182
135	42	232.210526315789	-190.210526315789
136	528	232.210526315789	295.789473684211
137	0	232.210526315789	-232.210526315789
138	1122	1175.11538461538	-53.1153846153845
139	1305	1175.11538461538	129.884615384615
140	81	232.210526315789	-151.210526315789
141	262	232.210526315789	29.7894736842105
142	1104	1175.11538461538	-71.1153846153845
143	1290	1175.11538461538	114.884615384615
144	1248	1371.45454545455	-123.454545454545

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 0 ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = 0 ; par4 = no ;

R code (references can be found in the software module):

library(party)
library(Hmisc)
par1 <- as.numeric(par1)
par3 <- as.numeric(par3)
x <- data.frame(t(y))
is.data.frame(x)
x <- x[!is.na(x[,par1]),]
k <- length(x[1,])
n <- length(x[,1])
colnames(x)[par1]
x[,par1]
if (par2 == 'kmeans') {
cl <- kmeans(x[,par1], par3)
print(cl)
clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
clm <- clm[sort.list(clm[,1]),]
for (i in 1:par3) {
cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')
}
cl$cluster <- as.factor(cl$cluster)
print(cl$cluster)
x[,par1] <- cl$cluster
}
if (par2 == 'quantiles') {
x[,par1] <- cut2(x[,par1],g=par3)
}
if (par2 == 'hclust') {
hc <- hclust(dist(x[,par1])^2, 'cen')
print(hc)
memb <- cutree(hc, k = par3)
dum <- c(mean(x[memb==1,par1]))
for (i in 2:par3) {
dum <- c(dum, mean(x[memb==i,par1]))
}
hcm <- matrix(cbind(dum,1:par3),ncol=2)
hcm <- hcm[sort.list(hcm[,1]),]
for (i in 1:par3) {
memb[memb==hcm[i,2]] <- paste('C',i,sep='')
}
memb <- as.factor(memb)
print(memb)
x[,par1] <- memb
}
if (par2=='equal') {
ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))
x[,par1] <- as.factor(ed)
}
table(x[,par1])
colnames(x)
colnames(x)[par1]
x[,par1]
if (par2 == 'none') {
m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
}
load(file='createtable')
if (par2 != 'none') {
m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
if (par4=='yes') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Prediction (training)',par3+1,TRUE)
a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Actual',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
a<-table.row.end(a)
for (i in 1:10) {
ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
if (i==1) {
m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
m.ct.i.actu <- x[ind==1,par1]
m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
m.ct.x.actu <- x[ind==2,par1]
} else {
m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
}
}
print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
numer <- numer + m.ct.i.tab[i,i]
}
print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
numer <- numer + m.ct.x.tab[i,i]
}
print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'Overall',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.i.cp,4))
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.x.cp,4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
}
}
m
bitmap(file='test1.png')
plot(m)
dev.off()
bitmap(file='test1a.png')
plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
dev.off()
if (par2 == 'none') {
forec <- predict(m)
result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
colnames(result) <- c('Actuals','Forecasts','Residuals')
print(result)
}
if (par2 != 'none') {
print(cbind(as.factor(x[,par1]),predict(m)))
myt <- table(as.factor(x[,par1]),predict(m))
print(myt)
}
bitmap(file='test2.png')
if(par2=='none') {
op <- par(mfrow=c(2,2))
plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
par(op)
}
if(par2!='none') {
plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
}
dev.off()
if (par2 == 'none') {
detcoef <- cor(result$Forecasts,result$Actuals)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goodness of Fit',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.element(a,round(detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'R-squared',1,TRUE)
a<-table.element(a,round(detcoef*detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'RMSE',1,TRUE)
a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Actuals',header=TRUE)
a<-table.element(a,'Forecasts',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(result$Actuals)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,result$Actuals[i])
a<-table.element(a,result$Forecasts[i])
a<-table.element(a,result$Residuals[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
}
if (par2 != 'none') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
for (i in 1:par3) {
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (j in 1:par3) {
a<-table.element(a,myt[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code